Defect Backlog Size Prediction for Open-Source Projects with the Autoregressive Moving Average and Exponential Smoothing Models
Paper in proceeding, 2023

Context: predicting the number of defects in a defect backlog in a given time horizon can help allocate project resources and organize software development.
Goal: to compare the accuracy of three defect backlog prediction methods in the context of large open-source (OSS) projects, i.e., ARIMA, Exponential Smoothing (ETS), and the state-of-the-art method developed at Ericsson AB (MS).
Method: we perform a simulation study on a sample of 20 open-source projects to compare the prediction accuracy of the methods. Also, we use the Naïve prediction method as a baseline for sanity check. We use statistical inference tests and effect size coefficients to compare the prediction errors. Results: ARIMA, ETS, and MS were more accurate than the Naïve method. Also, the prediction errors were statistically lower for ETS than for MS (however, the effect size was negligible).
Conclusions: ETS seems slightly more accurate than MS when predicting defect backlog size of OSS projects.

Author

Paulina Aniola Sielicka

Sushant Kumar Pandey

University of Gothenburg

Software Engineering 1

Miroslaw Staron

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

University of Gothenburg

Miroslaw ochodek

Poznan University of Technology

Proceedings of the 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023

83-92
978-83-967447-8-4 (ISBN)

18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023
Warsaw, Poland,

Subject Categories (SSIF 2025)

Software Engineering

Computer Sciences

DOI

10.15439/2023F5474

More information

Latest update

6/26/2025